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[2602.13280] BEAGLE: Behavior-Enforced Agent for Grounded Learner Emulation
Summary
The paper presents BEAGLE, a neuro-symbolic framework that simulates student learning behaviors in open-ended problem-solving environments, addressing competency bias in LLMs.
Why It Matters
BEAGLE's approach to simulating authentic student learning behaviors is significant for educational research and adaptive tutoring systems. By overcoming the limitations of traditional models, it enhances the understanding of student learning processes and supports the development of more effective educational technologies.
Key Takeaways
- BEAGLE integrates Self-Regulated Learning theory to improve student behavior simulation.
- The framework uses a semi-Markov model for cognitive and metacognitive behavior transitions.
- Bayesian Knowledge Tracing with flaw injection creates realistic knowledge gaps.
- BEAGLE's design separates strategy use from code generation to enhance learning authenticity.
- Evaluation shows BEAGLE outperforms existing models in simulating real student data.
Computer Science > Artificial Intelligence arXiv:2602.13280 (cs) [Submitted on 6 Feb 2026] Title:BEAGLE: Behavior-Enforced Agent for Grounded Learner Emulation Authors:Hanchen David Wang, Clayton Cohn, Zifan Xu, Siyuan Guo, Gautam Biswas, Meiyi Ma View a PDF of the paper titled BEAGLE: Behavior-Enforced Agent for Grounded Learner Emulation, by Hanchen David Wang and 5 other authors View PDF HTML (experimental) Abstract:Simulating student learning behaviors in open-ended problem-solving environments holds potential for education research, from training adaptive tutoring systems to stress-testing pedagogical interventions. However, collecting authentic data is challenging due to privacy concerns and the high cost of longitudinal studies. While Large Language Models (LLMs) offer a promising path to student simulation, they suffer from competency bias, optimizing for efficient correctness rather than the erratic, iterative struggle characteristic of novice learners. We present BEAGLE, a neuro-symbolic framework that addresses this bias by incorporating Self-Regulated Learning (SRL) theory into a novel architecture. BEAGLE integrates three key technical innovations: (1) a semi-Markov model that governs the timing and transitions of cognitive behaviors and metacognitive behaviors; (2) Bayesian Knowledge Tracing with explicit flaw injection to enforce realistic knowledge gaps and "unknown unknowns"; and (3) a decoupled agent design that separates high-level strategy use from code...
